Development of Local Feature Extraction and Reduction Schemes for Iris Biometrics

Abstract

Iris is one of the most reliable biometric trait used for human recognition due to its stability and randomness. Typically, recognition concerns with the matching of the features extracted from the iris regions. A feature extraction method can be categorized as local or global, depending on the manner in which the features are extracted from an image. In case of global features fail to represent details of an image because, the computation is focused on the image as a whole. On the contrary, local features are more precise and capable of representing the details of an image as they are computed from specific regions of the image. In the conventional
approaches, the local features consider corners as keypoints, that may not always be suitable for iris images. Salient regions are visually pre-attentive distinct portions in an image and are appropriate candidate for interest points. The thesis presents a salient keypoint detector called Salient Point of Interest using Entropy (SPIE). Entropy from local segments are used as the significant measure of saliency. In order to compute the entropy value of such portions, an entropy map is generated. Scale invariance property of the detector is achieved by constructing the scale-space for the input image. Generally local feature extraction methods suffer from high dimensionality. Thus, they are computationally expensive and unsuitable for real-time application. Some reduction techniques can be applied to decrease the feature size and increase the computational speed. In this thesis, feature reduction is achieved by decreasing the number of keypoints using density-based clustering. The proposed method reduces keypoints efficiently, by grouping all the closely placed keypoints into one. Each cluster is then represented by a keypoint with its scale and location, for which an algorithm is presented. The proposed schemes are validated through publicly available databases, which shows the superiority of the proposed ones over the existing state-of-the-art methods.